AB xlmtools
Live data and actions for Stellar (XLM, Soroban, DEX, USDC), plus crypto prices, stock quotes, weather, domain checks, web search, deep research, screenshots, URL scraping, AI images, YouTube. Skip for general knowledge, math, code, or "what is X" explainers. Make sure to use this skill whenever the user mentions Stellar, XLM, Soroban, Lumens, "latest" anything, live prices, current events, or a URL to fetch — even if they don't say "XLMTools" by name. DO NOT TRIGGER for questions answerable from training data alone, explanations of concepts, or summarizing text the user pasted. Available as MCP (tools prefixed `mcp__xlmtools__*`) or terminal CLI (`xlm`). Paid tools cost $0.001–$0.04 USDC.
As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 68/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 100Steps. 35 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 19 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3557 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 16 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 698: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.